This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD emerges as a secure and efficient method for identifying fraudulent transactions within the financial sector. Implementing a dual-phase training model across distributed clients enhances data integrity and enables superior performance metrics, achieving precision rates consistently above 95%. Additionally, QFNN-FFD demonstrates exceptional resilience by maintaining an impressive 80% accuracy, highlighting its robustness and readiness for real-world applications. This combination of high performance, security, and robustness against noise positions QFNN-FFD as a transformative advancement in financial technology solutions and establishes it as a new benchmark for privacy-focused fraud detection systems. This framework facilitates the broader adoption of secure, quantum-enhanced financial services and inspires future innovations that could use QML to tackle complex challenges in other areas requiring high confidentiality and accuracy.

翻译：本研究提出面向金融欺诈检测的量子联邦神经网络（QFNN-FFD），这是一个融合量子机器学习（QML）、量子计算与联邦学习（FL）的前沿框架，专用于金融欺诈检测。该框架利用量子技术的强大计算能力及联邦学习提供的稳健数据隐私保护，成为金融领域识别欺诈交易的安全高效方法。通过分布式客户端上实现的双阶段训练模型，QFNN-FFD增强了数据完整性，并取得了持续超过95%的精确率等卓越性能指标。此外，QFNN-FFD展现了卓越的抗噪能力，保持高达80%的准确率，凸显了其稳健性与实际应用部署的准备度。这种高性能、高安全性及抗噪声鲁棒性的结合，使QFNN-FFD成为金融技术解决方案中的变革性进展，并确立了隐私保护导向欺诈检测系统的新标杆。该框架促进了安全、量子增强型金融服务的更广泛采用，并激励未来创新——借助QML解决其他需高保密性与高精度的复杂领域挑战。